A macro used internally to check for errors and goto fail if
found. This macro requires a fail label in the current code
block. The loop variable must have at least members (obj,
errormask, and errorobj). If loop ->obj is nonzero, then
PyErr_Occurred () is called (meaning the GIL must be held). If
loop ->obj is zero, then if loop ->errormask is nonzero,
PyUFunc_checkfperr is called with arguments loop ->errormask
and loop ->errobj. If the result of this check of the IEEE
floating point registers is true then the code redirects to the
fail label which must be defined.

A macro that expands to platform-dependent code. The ret
variable can can be any integer. The UFUNC_FPE_{ERR} bits are
set in ret according to the status of the corresponding error
flags of the floating point processor.

Create a new broadcasting universal function from required variables.
Each ufunc builds around the notion of an element-by-element
operation. Each ufunc object contains pointers to 1-d loops
implementing the basic functionality for each supported type.

Note

The func, data, types, name, and doc arguments are not
copied by PyUFunc_FromFuncAndData. The caller must ensure
that the memory used by these arrays is not freed as long as the
ufunc object is alive.

Parameters:

func – Must to an array of length ntypes containing
PyUFuncGenericFunction items. These items are pointers to
functions that actually implement the underlying
(element-by-element) function times.

data – Should be NULL or a pointer to an array of size ntypes
. This array may contain arbitrary extra-data to be passed to
the corresponding 1-d loop function in the func array.

types – Must be of length (nin + nout) * ntypes, and it
contains the data-types (built-in only) that the corresponding
function in the func array can deal with.

ntypes – How many different data-type “signatures” the ufunc has implemented.

nin – The number of inputs to this operation.

nout – The number of outputs

identity – XXX: Undocumented

name – The name for the ufunc. Specifying a name of ‘add’ or
‘multiply’ enables a special behavior for integer-typed
reductions when no dtype is given. If the input type is an
integer (or boolean) data type smaller than the size of the int_
data type, it will be internally upcast to the int_ (or uint)
data type.

doc – Allows passing in a documentation string to be stored with the
ufunc. The documentation string should not contain the name
of the function or the calling signature as that will be
dynamically determined from the object and available when
accessing the __doc__ attribute of the ufunc.

check_return – Unused and present for backwards compatibility of the C-API. A
corresponding check_return integer does exist in the ufunc
structure and it does get set with this value when the ufunc
object is created.

This function allows the user to register a 1-d loop with an
already- created ufunc to be used whenever the ufunc is called
with any of its input arguments as the user-defined
data-type. This is needed in order to make ufuncs work with
built-in data-types. The data-type must have been previously
registered with the numpy system. The loop is passed in as
function. This loop can take arbitrary data which should be
passed in as data. The data-types the loop requires are passed
in as arg_types which must be a pointer to memory at least as
large as ufunc->nargs.

Replace a 1-d loop matching the given signature in the
already-created ufunc with the new 1-d loop newfunc. Return the
old 1-d loop function in oldfunc. Return 0 on success and -1 on
failure. This function works only with built-in types (use
PyUFunc_RegisterLoopForType for user-defined types). A
signature is an array of data-type numbers indicating the inputs
followed by the outputs assumed by the 1-d loop.

A generic ufunc call. The ufunc is passed in as self, the
arguments to the ufunc as args. The mps argument is an array
of PyArrayObject pointers containing the converted input
arguments as well as the ufunc outputs on return. The user is
responsible for managing this array and receives a new reference
for each array in mps. The total number of arrays in mps is
given by self ->nin + self ->nout.

A simple interface to the IEEE error-flag checking support. The
errmask argument is a mask of UFUNC_MASK_{ERR} bitmasks
indicating which errors to check for (and how to check for
them). The errobj must be a Python tuple with two elements: a
string containing the name which will be used in any communication
of error and either a callable Python object (call-back function)
or Py_None. The callable object will only be used if
UFUNC_ERR_CALL is set as the desired error checking
method. This routine manages the GIL and is safe to call even
after releasing the GIL. If an error in the IEEE-compatibile
hardware is determined a -1 is returned, otherwise a 0 is
returned.

Get the Python values used for ufunc processing from the
thread-local storage area unless the defaults have been set in
which case the name lookup is bypassed. The name is placed as a
string in the first element of *errobj. The second element is
the looked-up function to call on error callback. The value of the
looked-up buffer-size to use is passed into bufsize, and the
value of the error mask is placed into errmask.

At the core of every ufunc is a collection of type-specific functions
that defines the basic functionality for each of the supported types.
These functions must evaluate the underlying function
times. Extra-data may be passed in that may be used during the
calculation. This feature allows some general functions to be used as
these basic looping functions. The general function has all the code
needed to point variables to the right place and set up a function
call. The general function assumes that the actual function to call is
passed in as the extra data and calls it with the correct values. All
of these functions are suitable for placing directly in the array of
functions stored in the functions member of the PyUFuncObject
structure.

Type specific, core 1-d functions for ufuncs where each
calculation is obtained by calling a function taking one input
argument and returning one output. This function is passed in
func. The letters correspond to dtypechar’s of the supported
data types ( f - float, d - double, g - long double,
F - cfloat, D - cdouble, G - clongdouble). The
argument func must support the same signature. The _As_X_X
variants assume ndarray’s of one data type but cast the values to
use an underlying function that takes a different data type. Thus,
PyUFunc_f_f_As_d_d uses ndarrays of data type NPY_FLOAT
but calls out to a C-function that takes double and returns
double.

Type specific, core 1-d functions for ufuncs where each
calculation is obtained by calling a function taking two input
arguments and returning one output. The underlying function to
call is passed in as func. The letters correspond to
dtypechar’s of the specific data type supported by the
general-purpose function. The argument func must support the
corresponding signature. The _As_XX_X variants assume ndarrays
of one data type but cast the values at each iteration of the loop
to use the underlying function that takes a different data type.

One-input, one-output, and two-input, one-output core 1-d functions
for the NPY_OBJECT data type. These functions handle reference count
issues and return early on error. The actual function to call is func
and it must accept calls with the signature (PyObject*)(PyObject*)
for PyUFunc_O_O or (PyObject*)(PyObject*,PyObject*)
for PyUFunc_OO_O.

This general purpose 1-d core function assumes that func is a string
representing a method of the input object. For each
iteration of the loop, the Python obejct is extracted from the array
and its func method is called returning the result to the output array.

This general purpose 1-d core function assumes that func is a
string representing a method of the input object that takes one
argument. The first argument in args is the method whose function is
called, the second argument in args is the argument passed to the
function. The output of the function is stored in the third entry
of args.

At each iteration of the loop, the nin input objects are exctracted
from their object arrays and placed into an argument tuple, the Python
callable is called with the input arguments, and the nout
outputs are placed into their object arrays.

These are the constants and functions for accessing the ufunc
C-API from extension modules in precisely the same way as the
array C-API can be accessed. The import_ufunc () function must
always be called (in the initialization subroutine of the
extension module). If your extension module is in one file then
that is all that is required. The other two constants are useful
if your extension module makes use of multiple files. In that
case, define PY_UFUNC_UNIQUE_SYMBOL to something unique to
your code and then in source files that do not contain the module
initialization function but still need access to the UFUNC API,
define PY_UFUNC_UNIQUE_SYMBOL to the same name used previously
and also define NO_IMPORT_UFUNC.

The C-API is actually an array of function pointers. This array is
created (and pointed to by a global variable) by import_ufunc. The
global variable is either statically defined or allowed to be seen
by other files depending on the state of
Py_UFUNC_UNIQUE_SYMBOL and NO_IMPORT_UFUNC.